Indigenous identity identification in administrative health care data globally: A scoping review
Bibliographic record
Abstract
OBJECTIVE: Both Indigenous and non-Indigenous governments and organizations have increasingly called for improved Indigenous health data in order to improve health equity among Indigenous peoples. This scoping review identifies best practices, potential consequences and barriers for advancing Indigenous health data and Indigenous data sovereignty globally. METHODS: A scoping review was conducted to capture the breadth and nature of the academic and grey literature. We searched academic databases for academic records published between 2000 and 2021. We used Google to conduct a review of the grey literature. We applied Harfield's Aboriginal and Torres Strait Islander Quality Appraisal Tool (QAT) to all original research articles included in the review to assess the quality of health information from an Indigenous perspective. RESULTS: In total, 77 academic articles and 49 grey literature records were included. Much of the academic literature was published in the last 12 years, demonstrating a more recent interest in Indigenous health data. Overall, we identified two ways for Indigenous health data to be retrieved. The first approach is health care organizations asking clients to voluntarily self-identify as Indigenous. The other approach is through data linkage. Both approaches to improving Indigenous health data require awareness of the intergenerational consequences of settler colonialism along with a general mistrust in health care systems among Indigenous peoples. This context also presents special considerations for health care systems that wish to engage with Indigenous communities around the intention, purpose, and uses of the identification of Indigenous status in administrative databases and in health care settings. Partnerships with local Indigenous nations should be developed prior to the systematic collection of Indigenous identifiers in health administrative data. The QAT revealed that many research articles do not include adequate information to describe how Indigenous communities and stakeholders have been involved in this research. CONCLUSION: There is consensus within the academic literature that improving Indigenous health should be of high priority for health care systems globally. To address data disparities, governments and health organizations are encouraged to work in collaboration with local Indigenous nations and stakeholders at every step from conceptualization, data collection, analysis, to ownership. This finding highlights the need for future research to provide transparent explanation of how meaningful Indigenous collaboration is achieved in their research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".